Class lectures: Mondays and Wednesdays 10:30-11:50am
in GHC 4307

Recitations: Thursdays 5:00 - 6:20, Hammerschlag Hall B103

Essentially, every problem in computer science and engineering can
be formulated as the optimization of some function under some set
of constraints. This universal reduction automatically suggests that
such optimization tasks are intractable. Fortunately, most real world
problems have special structure, such as convexity, locality,
decomposability or submodularity. These properties allow us to
formulate optimization problems that can often be solved efficiently.
This course is designed to give a graduate-level student a thorough
grounding in the formulation of optimization problems that exploit
such structure and in efficient solution methods for these problems.
The course focuses mainly on the formulation and solution of
convex and combinatorial optimization problems. These general
concepts will also be illustrated through applications in
machine learning, AI, computer vision and robotics.

Students entering the class should have a pre-existing working
knowledge of algorithms, though the class
has been designed to allow students with a strong numerate background
to catch up and fully participate. Though not required, having taken
10-701 or an equivalent machine learning class will be helpful, since
we will use applications in machine learning and AI to demonstrate
the concepts we cover in class.

Grading

Homeworks (5 assignments 50%)

Final project (30%)

Final exam (20%) - Out: April 28th Due: May 3rd by Noon

Auditing

We don't know for sure yet whether we will be able to allow
auditors. If you are considering auditing, you should attend the
first class. In any case, students wishing to audit must register to
audit the class. To satisfy the auditing requirement, you must
either:

Do *two* homeworks, and get at least 75% of the points in each; or

Take the final, and get at least 50% of the points; or

Do a class project and do *one* homework, and get at least 75% of the
points in the homework

Like any class project, it must address a topic related to machine
learning and you must have started the project while taking this
class (can't be something you did last semester). You will need to
submit a project proposal with everyone else, and present a poster
with everyone. You don't need to submit a milestone or final
paper. You must get at least 80% on the poster presentation part of
the project.

Please, send us an email saying that you will be auditing the class and
what you plan to do.

If you are not a student and want to sit in the class, please
get authorization from the instructors.

Homework policy

Important Note: As we often reuse problem set questions from
previous
years, or problems covered by papers and webpages, we expect the
students not to
copy, refer to, or look at the solutions in preparing their
answers. Since this is a graduate class, we expect students to want to
learn and not google for answers. The purpose of problem sets in this
class is to help you think about the material, not just give us the
right answers. Therefore, please restrict attention to the books
mentioned on the webpage when solving problems on the problem set. If
you do happen to use other material, it must be acknowledged clearly
with a citation on the submitted solution.

Collaboration policy

Homeworks will be done individually: each student must hand in their
own answers. In addition, each student must write their own code in
the programming part of the assignment. It is acceptable, however, for
students to collaborate in figuring out answers and helping each other
solve the problems. We will be assuming that, as participants in a
graduate course, you will be taking the responsibility to make sure
you personally understand the solution to any work arising from such
collaboration. In preparing your own writeup, you should not refer to
any written materials from a joint study session. You also must
indicate on each homework with whom you collaborated. The final
project may be completed individually or in teams of two students.

Late homework policy

Homeworks are due at the begining of class, unless otherwise specified.

You will be allowed 3 total late days without penalty for the entire
semester. For instance, you may be late by 1 day on three different
homeworks or late by 3 days on one homework. Each late day
corresponds to 24 hours or part thereof. Once those days are used,
you will be penalized according to the policy below:

Homework is worth full credit at the beginning of class on the due date.

For the next 24 hours, it will be graded so that the highest
possible score is equal to the 70th percentile of the distribution
of scores of the
on-time homeworks. For example, if your raw score is 80 out of 100
points, but the 70th percentile of the HW distribution is 85/100,
then you will get credit for 80 * (85/100) = 68 points.

For the following 24 hours, it will be graded out of the 40th
percentile of the on-time homeworks.

Thereafter, it will be graded out of the 10th
percentile.

You must turn in all of the homeworks, even if for reduced credit,
in order to pass the course. For very-late homeworks, it is
your responsibility to avoid looking at each solution set until after
you have turned in the corresponding homework.

Turn in all late homework assignments to Michelle (GHC 8001)
.

Homework regrades policy

If you feel that we have made an error in grading your homework,
please turn in your homework with a written explanation to Monica, and
we will consider your request. Please note that regrading of a
homework may cause your grade to go up or down.

Final project

Project proposal due date March 3rd in class (strict deadline, no late days allowed).

Graded milestone 1 due date March 24th in class (15% of project grade)

Graded milestone 2 due date April 21st in class (15% of project grade).

Poster session, May 7th 3-6pm in the NSH Atrium (20%
of project grade)

Paper due date - May 10th (Noon, no late days) (via electronic submission
to the instructors list) (50% of project grade)

For project milestone, roughly half of the project work should be
completed. A short, graded write-up will be required, and we will
provide feedback.

Note to people outside CMU

Feel free to use the slides and materials
available online here. If you use our slides, an appropriate
attribution is requested.
Please email the instructors with any corrections or
improvements.